ArticleMedical image analysis2024
Encrypted federated learning for secure decentralized collaboration in cancer image analysis.
Article in Medical image analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- Review
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- RepFed-Net: privacy-preserving federated learning with reputation-aware aggregation for retinal vessel segmentation using enhanced U-Net.Scientific reports · 2026Article
- Federated Learning in Healthcare Ethics: A Systematic Review of Privacy-Preserving and Equitable Medical AI.Healthcare (Basel, Switzerland) · 2026Review
- Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.NPJ digital medicine · 2026Article
- Artificial Intelligence-Enabled Multi-Omics for Predicting Immune Checkpoint Inhibitor Response and Resistance.Journal of multidisciplinary healthcare · 2026Review
- Ethical challenges and governance of artificial intelligence in hepatocellular carcinoma management : Wan DL et al. Ethical considerations in AI for hepatocellular carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- A Collaborative Data Sharing Platform to Accelerate Translation of Biomedical Innovations.Bioengineering (Basel, Switzerland) · 2025Article
- A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.Scientific reports · 2025Article
- Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.Radiology. Artificial intelligence · 2025Review
- Real-world radiology data for artificial intelligence-driven cancer support systems and biomarker development.ESMO real world data and digital oncology · 2025Review
- A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors.Scientific reports · 2025Article
- An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation.Scientific reports · 2025Article
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- Enhancing privacy-preserving brain tumor classification with adaptive reputation-aware federated learning and homomorphic encryption.PeerJ. Computer science · 2025Article
- Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.Briefings in bioinformatics · 2024Review
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- Investigation of distributed learning for automated lesion detection in head MR images.Radiological physics and technology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.